The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Marc Vidal - One of the best experts on this subject based on the ideXlab platform.
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domain based prediction of the human isoform Interactome provides insights into the functional impact of alternative splicing
PLOS Computational Biology, 2017Co-Authors: Mohamed Ghadie, Marc Vidal, Luke Lambourne, Yu XiaAbstract:Alternative splicing is known to remodel protein-protein interaction networks ("Interactomes"), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform Interactome from the reference Interactome. First, we construct the domain-resolved reference Interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform Interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform Interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with Interactomes provides insights into the functional impact of alternative splicing.
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uncovering disease disease relationships through the incomplete Interactome
Science, 2015Co-Authors: J. Menche, Marc Vidal, Amitabh Sharma, Maksim Kitsak, Susan Dina Ghiassian, Joseph Loscalzo, Albert-lászló BarabásiAbstract:According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human Interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the Interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an Interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.
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Interactome networks and human disease
Cell, 2011Co-Authors: Marc Vidal, Albert-lászló Barabási, Michael E CusickAbstract:Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of Interactome networks and the insights that can come from analyzing them. We elaborate on why Interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from Interactome network models, and how these properties may relate to human disease.
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high quality binary Interactome mapping
Methods in Enzymology, 2010Co-Authors: Matija Dreze, Marc Vidal, Michael E Cusick, Dario Monachello, Claire Lurin, David E Hill, Pascal BraunAbstract:Physical interactions mediated by proteins are critical for most cellular functions and altogether form a complex macromolecular "Interactome" network. Systematic mapping of protein-protein, protein-DNA, protein-RNA, and protein-metabolite interactions at the scale of the whole proteome can advance understanding of Interactome networks with applications ranging from single protein functional characterization to discoveries on local and global systems properties. Since the early efforts at mapping protein-protein Interactome networks a decade ago, the field has progressed rapidly giving rise to a growing number of Interactome maps produced using high-throughput implementations of either binary protein-protein interaction assays or co-complex protein association methods. Although high-throughput methods are often thought to necessarily produce lower quality information than low-throughput experiments, we have recently demonstrated that proteome-scale Interactome datasets can be produced with equal or superior quality than that observed in literature-curated datasets derived from large numbers of small-scale experiments. In addition to performing all experimental steps thoroughly and including all necessary controls and quality standards, careful verification of all interacting pairs and validation tests using independent, orthogonal assays are crucial to ensure the release of Interactome maps of the highest possible quality. This chapter describes a high-quality, high-throughput binary protein-protein Interactome mapping pipeline that includes these features.
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effect of sampling on topology predictions of protein protein interaction networks
Nature Biotechnology, 2005Co-Authors: Denis Dupuy, Jingdong J Han, Nicolas Bertin, Michael E Cusick, Marc VidalAbstract:Currently available protein-protein interaction (PPI) network or 'Interactome' maps, obtained with the yeast two-hybrid (Y2H) assay or by co-affinity purification followed by mass spectrometry (co-AP/MS), only cover a fraction of the complete PPI networks. These partial networks display scale-free topologies–most proteins participate in only a few interactions whereas a few proteins have many interaction partners. Here we analyze whether the scale-free topologies of the partial networks obtained from Y2H assays can be used to accurately infer the topology of complete Interactomes. We generated four theoretical interaction networks of different topologies (random, exponential, power law, truncated normal). Partial sampling of these networks resulted in sub-networks with topological characteristics that were virtually indistinguishable from those of currently available Y2H-derived partial Interactome maps. We conclude that given the current limited coverage levels, the observed scale-free topology of existing Interactome maps cannot be confidently extrapolated to complete Interactomes.
Nicolas Simonis - One of the best experts on this subject based on the ideXlab platform.
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empirically controlled mapping of the caenorhabditis elegans protein protein Interactome network
Nature Methods, 2009Co-Authors: Nicolas Simonis, Julie Sahalie, Irma Lemmens, Anne Ruxandra Carvunis, Murat Tasan, Jean Francois Rual, Tomoko Hirozanekishikawa, Tong Hao, Kavitha Venkatesan, Fana GebreabAbstract:High-throughput yeast two-hybrid screening is used to generate the largest C. elegans Interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al., also online, the data set is evaluated for quality and is used to estimate the total size of the worm Interactome.
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Empirically controlled mapping of the Caenorhabditis elegans protein-protein Interactome network
Nature Methods, 2009Co-Authors: Nicolas Simonis, Julie Sahalie, Tomoko Hirozane-kishikawa, Irma Lemmens, Anne Ruxandra Carvunis, Murat Tasan, Jean Francois Rual, Tong Hao, Kavitha Venkatesan, Fana GebreabAbstract:High-throughput yeast two-hybrid screening is used to generate the largest C. elegans Interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al ., also online, the data set is evaluated for quality and is used to estimate the total size of the worm Interactome. To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards. We present an expanded C. elegans protein-protein interaction network, or 'Interactome' map, derived from testing a matrix of ∼10,000 × ∼10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm Interactome at ∼116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins.
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a protein domain based Interactome network for c elegans early embryogenesis
Cell, 2008Co-Authors: Mike Boxem, Irma Lemmens, Zoltan Maliga, Niels Klitgord, Miyeko Mana, Lorenzo De Lichtervelde, Joram D Mul, Diederik Van De Peut, Maxime Devos, Nicolas SimonisAbstract:Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "Interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based Interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete Interactome network. This Interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.
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High-quality binary protein interaction map of the yeast Interactome network
Science, 2008Co-Authors: Haiyuan Yu, Muhammed A. Yildirim, Julie Sahalie, Tomoko Hirozane-kishikawa, Irma Lemmens, Pascal Braun, Fana Gebreab, K Venkatesan, Na Li, Nicolas SimonisAbstract:Current yeast Interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast Interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary Interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex Interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.
Irma Lemmens - One of the best experts on this subject based on the ideXlab platform.
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A proteome-scale map of the human Interactome network
Cell, 2014Co-Authors: Thomas Rolland, Nidhi Sahni, Celia Fontanillo, S. J. Pevzner, Irma Lemmens, Song Yi, Quan Zhong, Benoit Charloteaux, Murat Tasan, Roberto MoscaAbstract:Just as reference genome sequences revolutionized human genetics, reference maps of Interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ∼14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ∼30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human Interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality Interactome models will help "connect the dots" of the genomic revolution.
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empirically controlled mapping of the caenorhabditis elegans protein protein Interactome network
Nature Methods, 2009Co-Authors: Nicolas Simonis, Julie Sahalie, Irma Lemmens, Anne Ruxandra Carvunis, Murat Tasan, Jean Francois Rual, Tomoko Hirozanekishikawa, Tong Hao, Kavitha Venkatesan, Fana GebreabAbstract:High-throughput yeast two-hybrid screening is used to generate the largest C. elegans Interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al., also online, the data set is evaluated for quality and is used to estimate the total size of the worm Interactome.
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an empirical framework for binary Interactome mapping
Nature Methods, 2009Co-Authors: Irma Lemmens, Jean Francois Rual, Tomoko Hirozanekishikawa, Tong Hao, Kavitha Venkatesan, Alexei Vazquez, Ulrich Stelzl, Martina ZenknerAbstract:Several attempts have been made to systematically map protein-protein interaction, or 'Interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human Interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human Interactome. We estimate that the human Interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and Interactome size are crucial to establish the magnitude of the task of comprehensive human Interactome mapping and to elucidate a path toward this goal.
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Empirically controlled mapping of the Caenorhabditis elegans protein-protein Interactome network
Nature Methods, 2009Co-Authors: Nicolas Simonis, Julie Sahalie, Tomoko Hirozane-kishikawa, Irma Lemmens, Anne Ruxandra Carvunis, Murat Tasan, Jean Francois Rual, Tong Hao, Kavitha Venkatesan, Fana GebreabAbstract:High-throughput yeast two-hybrid screening is used to generate the largest C. elegans Interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al ., also online, the data set is evaluated for quality and is used to estimate the total size of the worm Interactome. To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards. We present an expanded C. elegans protein-protein interaction network, or 'Interactome' map, derived from testing a matrix of ∼10,000 × ∼10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm Interactome at ∼116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins.
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a protein domain based Interactome network for c elegans early embryogenesis
Cell, 2008Co-Authors: Mike Boxem, Irma Lemmens, Zoltan Maliga, Niels Klitgord, Miyeko Mana, Lorenzo De Lichtervelde, Joram D Mul, Diederik Van De Peut, Maxime Devos, Nicolas SimonisAbstract:Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "Interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based Interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete Interactome network. This Interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.
Fana Gebreab - One of the best experts on this subject based on the ideXlab platform.
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Next-generation sequencing to generate Interactome datasets
Nature Methods, 2011Co-Authors: Haiyuan Yu, Leah Tardivo, Stanley Tam, Evan Weiner, Changyu Fan, Nenad Svrzikapa, Tomoko Hirozane-kishikawa, Edward Rietman, Fana Gebreab, Xinping YangAbstract:Next-generation sequencing has not been applied to protein-protein Interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel Interactome-mapping pipeline, Stitch-seq, that combines PCR stitching with next-generation sequencing and used it to generate a new human Interactome dataset. Stitch-seq is applicable to various interaction assays and should help expand Interactome network mapping.
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empirically controlled mapping of the caenorhabditis elegans protein protein Interactome network
Nature Methods, 2009Co-Authors: Nicolas Simonis, Julie Sahalie, Irma Lemmens, Anne Ruxandra Carvunis, Murat Tasan, Jean Francois Rual, Tomoko Hirozanekishikawa, Tong Hao, Kavitha Venkatesan, Fana GebreabAbstract:High-throughput yeast two-hybrid screening is used to generate the largest C. elegans Interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al., also online, the data set is evaluated for quality and is used to estimate the total size of the worm Interactome.
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Empirically controlled mapping of the Caenorhabditis elegans protein-protein Interactome network
Nature Methods, 2009Co-Authors: Nicolas Simonis, Julie Sahalie, Tomoko Hirozane-kishikawa, Irma Lemmens, Anne Ruxandra Carvunis, Murat Tasan, Jean Francois Rual, Tong Hao, Kavitha Venkatesan, Fana GebreabAbstract:High-throughput yeast two-hybrid screening is used to generate the largest C. elegans Interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al ., also online, the data set is evaluated for quality and is used to estimate the total size of the worm Interactome. To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards. We present an expanded C. elegans protein-protein interaction network, or 'Interactome' map, derived from testing a matrix of ∼10,000 × ∼10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm Interactome at ∼116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins.
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High-quality binary protein interaction map of the yeast Interactome network
Science, 2008Co-Authors: Haiyuan Yu, Muhammed A. Yildirim, Julie Sahalie, Tomoko Hirozane-kishikawa, Irma Lemmens, Pascal Braun, Fana Gebreab, K Venkatesan, Na Li, Nicolas SimonisAbstract:Current yeast Interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast Interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary Interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex Interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.
Kavitha Venkatesan - One of the best experts on this subject based on the ideXlab platform.
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empirically controlled mapping of the caenorhabditis elegans protein protein Interactome network
Nature Methods, 2009Co-Authors: Nicolas Simonis, Julie Sahalie, Irma Lemmens, Anne Ruxandra Carvunis, Murat Tasan, Jean Francois Rual, Tomoko Hirozanekishikawa, Tong Hao, Kavitha Venkatesan, Fana GebreabAbstract:High-throughput yeast two-hybrid screening is used to generate the largest C. elegans Interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al., also online, the data set is evaluated for quality and is used to estimate the total size of the worm Interactome.
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an empirical framework for binary Interactome mapping
Nature Methods, 2009Co-Authors: Irma Lemmens, Jean Francois Rual, Tomoko Hirozanekishikawa, Tong Hao, Kavitha Venkatesan, Alexei Vazquez, Ulrich Stelzl, Martina ZenknerAbstract:Several attempts have been made to systematically map protein-protein interaction, or 'Interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human Interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human Interactome. We estimate that the human Interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and Interactome size are crucial to establish the magnitude of the task of comprehensive human Interactome mapping and to elucidate a path toward this goal.
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Empirically controlled mapping of the Caenorhabditis elegans protein-protein Interactome network
Nature Methods, 2009Co-Authors: Nicolas Simonis, Julie Sahalie, Tomoko Hirozane-kishikawa, Irma Lemmens, Anne Ruxandra Carvunis, Murat Tasan, Jean Francois Rual, Tong Hao, Kavitha Venkatesan, Fana GebreabAbstract:High-throughput yeast two-hybrid screening is used to generate the largest C. elegans Interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al ., also online, the data set is evaluated for quality and is used to estimate the total size of the worm Interactome. To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards. We present an expanded C. elegans protein-protein interaction network, or 'Interactome' map, derived from testing a matrix of ∼10,000 × ∼10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm Interactome at ∼116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins.
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towards a proteome scale map of the human protein protein interaction network
Nature, 2005Co-Authors: Jean Francois Rual, Matija Dreze, Tomoko Hirozanekishikawa, Tong Hao, Kavitha Venkatesan, Amelie Dricot, Gabriel F Berriz, Francis D Gibbons, Nono Ayiviguedehoussou, Niels KlitgordAbstract:Systematic mapping of protein-protein interactions, or 'Interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of Interactome networks that relate to known biological properties, suggesting that a human Interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human Interactome project.